Comparison of Data Mining Techniques for Predicting Compressive Strength of Environmentally Friendly Concrete

With its growing emphasis on sustainability, the construction industry is increasingly interested in environmentally friendly concrete produced by using alternative and/or recycled waste materials. However, the wide application of such concrete is hindered by the lack of understanding of the impacts of these materials on concrete properties. This research investigates and compares the performance of nine data mining models in predicting the compressive strength of a new type of concrete containing three alternative materials as fly ash, Haydite lightweight aggregate, and portland limestone cement. These models include three advanced predictive models (multilayer perceptron, support vector machines, and Gaussian processes regression), four regression tree models (M5P, REPTree, M5-Rules, and decision stump), and two ensemble methods (additive regression and bagging) with each of the seven individual models used as the base classifier. The analytical results show that, with appropriate parameter settings, all of these models, except decision stump, achieved acceptable prediction performance. The ensemble methods improved the prediction accuracy of the four regression tree models but had less success on the other three advanced predictive models. The individual Gaussian processes regression model and its related ensemble models reached the highest prediction accuracy in comparison groups. The results of this paper offer valuable insights on improving the use of these models for property prediction of concrete.